Category: modelling

Stress and scenario testing are important risk assessment tools. They also provide useful ways to prepare in advance for adverse scenarios so that management doesn’t have to create everything from first principles when something similar occurs.

But trying to imagine scenarios, particularly very severe scenarios, isn’t straightforward. We don’t have many examples of very extreme events.

Some insurers will dream up scenarios from scratch. It’s also common to refer to prior events and run the current business through those dark days. The Global Financial Crisis is a favourite – how would our business manage under credit spread spikes, drying up of liquidity, equity fall markets, higher lapses, lower sales, higher retrenchment claims, higher individual and corporate defaults, switches of funds out of equities, early withdrawals and surrenders and increased call centre volumes?

There is a Mauritian insurer called Island Life. Best name ever for an insurance company.

I firmly believe in anthropogenic climate change. I am not an expert, but my reading has convinced me of the seriousness of the issues, the overwhelming evidence that it is humans at fault. Having young children makes me seriously concerned about our ability to remedy the mess we’re in for their sake.

Part of my very inwards focussed, selfish research was on the practical impact for where I live. Unfortunately for my cycling aspirations, this is current on the bottom slopes of Table Mountain. Fortunately for my flood risk, the same.

I had a discussion with a client recently about the virtues of ensuring data written into a data warehouse is rock solid and understood and well defined.

My training and experience has given me high confidence that this is the right way forward for typical actuarial data. Here I’m talking in force policy data files, movements, transactions, and so on. This is really well structured data that will be used many times by different people and can easily be processed once, “on write”, stored in the data warehouse to be reliably and simply retrieved whenever necessary. Continue reading “Do Data Lakes hide Loch Ness Monsters?”

A recent article in The Actuary magazine addressed whether “de-risking in members’ best interests?” I say “recent” even though it’s from August because I am a little behind on my The Actuary reading.

In the article, the authors demonstrate that by modelling the impact of covenant risk, optimal investment portfolios for Defined Benefit (DB) pensions actually have more risky assets than if this covenant risk is ignored.

The covenant they refer to is the obligation of the sponsor to make good deficits within the pension fund. Covenant risk then is the risk that the sponsor is unable (typically through its own insolvency) to make good on this promise.

On the surface it should seem counterintuitive that by modelling an additional risk to pensioners, the answer is to invest in riskier assets, thus increasing risk.

The explanation proffered by the authors is that the higher expected returns from riskier assets allow the fund to potentially build up surplus, thus reducing the risks of covenant failure.

An actuary I know once made me cringe by saying “It doesn’t matter how an Economic Scenario Generator is constructed, if it meets all the calibration tests then it’s fine. A machine learning black box is as good as any other model with the same scores.” The idea being that if the model outputs correctly reflect the calibration inputs, the martingale test worked and the number of simulations generated produced an acceptably low standard error then the model is fit for purpose and is as good as any other model with the same “test scores”.

This is an example of actuarial sloppiness and is of course quite wrong.

There are at least three clear reasons why the model could still be dangerously specified and inferior to a more coherently structured model with worse “test scores”.

The least concerning of the three is probably interpolation. We rarely have a complete set of calibration inputs. We might have equity volatility at 1 year, 3 year, 5 year and an assumed long-term point of 30 years as calibration inputs to our model. We will be using the model outputs for many other points and just because we confirmed that the output results are consistent with the calibration inputs says nothing about whether the 2 year or 10 year volatility are appropriate.

The second reason is related – extrapolation. We may well be using model outputs beyond the 30 year point for which we have a specific calibration. A second example would be the volatility skew implied by the model even if none were specified – a more subtle form of extrapolation

A typical counter to these first two concerns is to use a more comprehensive set of calibration tests. Consider the smoothness of the volatility surface and ensure that extrapolations beyond the last calibration point are sensible. Good ideas both, but already we are veering away from a simplified calibration test score world and introducing judgment (a good thing!) into the evaluation.

There are limits to the “expanded test” solution. A truly comprehensive set of tests might well be impossibly large if not infinite with increasing cost to this brute force approach.

The third is a function of how the ESG is used. Most likely, the model is being used to value a complex guarantee or exotic derivative with a set of pay-offs based on the results of the ESG. Two ESGs could have the same calibration test results, even calculating similar at-the-money option values but value path-dependent or otherwise more exotic or products very differently due to different serial correlations or untested higher moments.

It is unlikely that a far out-of-the-money binary option was part of the calibration inputs and tests. If it were, it is certain that another instrument with information to add from a complete market was excluded. The set of calibration inputs and tests can never be exhaustive.

It turns out there is an easier way to decreasing the risk that the interpolated and extrapolated financial outcomes of using the ESG are nonsense: Start with a coherent model structure for the ESG. By using a logical underlying model incorporating drivers and interactions that reflect what we know of the market, we bring much of what we would need to add in that enormous set of calibration tests into the model and increase the likelihood of usable ESG results.

I can only begin to imagine the data horrors of dealing with multiple insurers, multiple sources, multiple different data problems. The analysis they do is critically useful and, in technical terms, helluva interesting. I enjoyed the presentation at both the Cape Town and Johannesburg #LACseminar2013 just because there is such a rich data set and the analysis is fascinating.

I do hope they agree to my suggestion to put the entire, cleaned, anonymised data set available on the web. Different parties will want to analyse the data in different ways; there is simply no way the CSI Committee can perform every analysis and every piece of investigation that everyone might want. Making the data publicly available gives actuaries, students, academics and more the ability to perform their own analysis. And at basically no cost.

The other, slightly more defensive reason, is that mistakes do happen from time to time. I’m very aware of the topical R-R paper that was based on flawed analysis of underlying data. Mistakes happen all the time, and allowing anyone who wants to have access to the data to repeat or disprove calculations and analysis only makes the results more robust.

So, here’s hoping for open access mortality investigation data for all! And here’s thanking the CSI committee (past and current) for everything they have already done.

It’s amazing to test this yourself. Hold down the button of a garage door opener and try to use your vehicle lock / unlock button. It doesn’t work. Simple as that. The signal from the garden variety garage door opener blocks the signal of most (all?) vehicle remotes.

Well, to prevent the problem requires either a high tech revamp of the vehicle remote industry, or for owners to actually observe or check that their doors are in fact locked. Listening for the clack, watching the hazard lights flash or even physically testing that the door is locked.

These methods work perfectly and are only the tiniest bit of extra effort.

Relying on models to simply give the right answer without checking that it makes sense is likely to get your credibility burgled. Form high level initial expectations, hypothesize plausible outcomes, check sensitivity to assumptions, reproduce initial results and run step-wise through individual changes, get a second opinion and fresh eyes.

This all takes a little time in the short term and saves everything in the long term.